University Business - July/August 2012 - (Page 103)

would be a useful piece of information if a policy was under consideration to try to increase enrollment by increasing financial aid awards across the board. In this case, a more effective solution for growing enrollment would be to invest instead in increasing demand and building up the applicant pool rather than spending more money on financial aid. Talking Timing There’s the practical consideration of timing, as well. The perfect packaging policy won’t be of much use if it arrives two months after competitors’ financial aid awards have gone out. This is a case where it’s better to be 80 percent right and on time than 100 percent right and too late. Even if all of these conditions are met, though, there are inherent limitations to predictive modeling. A model is no better than the variables chosen (or available) to be included. The quality of the data is also an issue; in particular, the consistency with which data are reported from one year to the next. And even the best of models can only account for a portion of the variance to be explained in the enrollment behavior of any group of 18-year-olds. In addition, econometric models typically don’t take into account external factors like unanticipated changes in state grant funding or changes in competitors’ tuition pricing and aid policies. Finally, predictive models are always built on prior years’ data. So major changes in the makeup of the admitted student pool will limit the predictive accuracy of the model. In a discussion on constraints on the use of predictive modeling in financial aid, economist Michael Rizzo, director of the Alex- ander Hamilton Institute at the University of Rochester (N.Y.), notes that “what matters is the financial aid award (and other features) relative to competitors’ awards, and this is very, very hard to measure. At best, what we are hoping to do is include enough variation in our models to enable the other variables to capture some of this.” Despite all of these limitations, the science of predictive modeling, combined with the art of student recruitment, can bring a level of predictability to what may, at times, seem to be a random jumble of student decisions. There’s just no substitute for solid, databased decision making. Bill Berg is an enrollment management consultant at Scannell & Kurz, www.scannellkurz .com. He is the former vice president for enrollment at Furman University (S.C.). Solution Showcase A look at innovative products from leading vendors In Solution Showcase, University Business spotlights innovative solutions from leading higher ed vendors with 20-minute product videos. Featured this month: ALEKS: Improving student success in Higher Education Math through adaptive learning and remediation Developed following decades of Learning Space Theory research, ALEKS is the only online program that uses artificial intelligence to precisely identify and deliver individualized instruction on the exact topics each student is most ready to learn. To watch, go to www.universitybusiness.com/showcase universitybusiness.com July/August 2012 | 103 http://www.scannellkurz.com http://www.scannellkurz.com http://www.universitybusiness.com/showcase http://www.universitybusiness.com

Table of Contents for the Digital Edition of University Business - July/August 2012

University Business - July/August 2012
Contents
Editor’s Note
Ad Index
College Index
Behind the News
Viewpoint
Admissions
Models of Efficiency
Floor Plans
Consulting Guide
Limited Liability
Touch to Begin
A Sound Investment
Voluntary Retirement
Human Resources
Money Matters
What’s New
End Note

University Business - July/August 2012